Focus offers the following scaling methods.
The linear function scales data values equally from the input range to the output range. Other non-linear functions also perform compression and/or stretching.
The square function compresses the lower end of spectrum and stretches the upper end. The image gray levels are shifted towards the lower end so the image appears darker in comparison to the linearly scaled result.
The logarithmic and square root functions compress the upper end. The square root is stronger than logarithmic, so the image is made brighter.
The automatic normalized quantization method transforms a typical input image of a unimodal histogram into a near-symmetric, Gaussian-like distribution with the median input level transformed to the mid-point of the output range. The algorithm applies a smooth nonlinear function to gradually compress the extreme high or low portions of input range. The middle portion of the data range is mapped with little distortion. This method is recommended for image quantization to a lower number of gray levels. It is robust in handling 32-bit input images.
The equal-area quantization method maps an image to the output range with an equalized output histogram. For example, each output level has approximately the same number of pixels.
The general power function fine-tunes the amount of compression and shifting each way with a user-supplied exponent value. If the exponent is greater than 1, the effect is shifting down; if the exponent is less than 1, the effect is shifting up.
For both the input and output the entire range of digital numbers (DN) is used in determining the range. Using the Save As feature may be appropriate for scaling to 8-bit data, but situations may arise where you need control of the input and output range. If this is the case, you must use the SCALE algorithm in the Algorithm Library.
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